IFN-γ, IL-17A, IL-4, and IL-13: Potential Biomarkers for Prediction of the Effectiveness of Biologics in Psoriasis Patients

Biologics are widely used to treat moderate-to-severe psoriasis. However, we have unmet needs for predicting individual patient responses to biologics before starting psoriasis treatment. We investigate a reliable platform and biomarkers for predicting individual patient responses to biologics. In a cohort study between 2018 and 2023 from a referral center in Taiwan, twenty psoriasis patients with or without psoriatic arthritis who had ever experienced two or more biologics were enrolled. Peripheral blood mononuclear cells obtained from these patients were treated with Streptococcus pyogenes and different biologics. The PASI reduction rate was strongly correlated with the reduction rate in the IL-13 level (p = 0.001) and the ratios of IFN-γ to IL-13 (p < 0.001), IFN-γ to IL-4 (p = 0.019), and IL-17A to IL-13 (p = 0.001). The PASI reduction difference was strongly correlated with the difference in the IFN-γ level (p = 0.002), the difference in the ratios of IFN-γ to IL-4 (p = 0.041), the difference in the ratios of IFN-γ to IL-13 (p = 0.006), the difference in the ratios of IL-17A to IL-4 (p = 0.011), and the difference in the ratios of IL-17A to IL-13 (p = 0.029). The biomarkers IFN-γ, IL-13, IFN-γ/IL4, IFN-γ/IL13, IL-17A/IL-4, and IL-17A/IL-13 are representative of the effectiveness of psoriasis treatment.


Introduction
Psoriasis is an autoimmune skin inflammation disease with a prevalence between 0.14% and 5.32% worldwide [1,2].In recent decades, psoriasis treatment has substantially improved with the introduction of several new biologics [3,4].Psoriasis Area Severity Index (PASI) scores can be reduced by 90% from baseline (PASI 90) or 100% from baseline (PASI 100) with the use of biologics.The estimated PASI 90 response rates at 44 to 60 weeks after beginning treatment were 79% for risankizumab, 76.5% for guselkumab, 74.0% for brodalumab, 73.9% for ixekizumab, 71.3% for secukinumab, 52.4% for ustekinumab, 46.2% for adalimumab, 40.1% for infliximab, 33.4% for etanercept, and 16.0% for apremilast [5].These overall response rates, however, do not represent the real responses of individual patients to each biological agent.More information about selecting biologics was investigated, such as difficult-to-treat psoriasis areas, short-term effectiveness, and long-term drug survival on IL-17 and IL-23 inhibitors in biologics-naïve patients or after adalimumab failure [6][7][8].Biologics-naive patients weighing 65 kg could be considered potential candidates for a half dose of risankizumab in treating psoriasis [9].Additionally, crucial factors influencing an individual patient's clinical response include the patient's genetics, environmental factors, and autoantibody profile, as well as whether the patient is biologics-naïve [10][11][12].In 2015, Boehncke et al. mentioned a need for a reliable platform for predicting individual patient responses to biologics before starting psoriasis treatment [13].
The medical treatment of psoriasis and psoriatic arthritis incurs substantial costs every year.Some patients do not respond favorably to a first clinical treatment with biological agents.These patients must then change to another biological agent to continue treatment.Due to the lack of a pre-administration evaluation and a screening platform, patients are often prescribed biological agents according to standard treatment guidelines instead of according to their individual cases.An improper selection of biologics causes the patient physical discomfort and psychological stress, as well as wasting health care resources.
A prescreen is the first step toward personal, precise treatment.Several studies investigated genes, including HLA-related and non-HLA-related single-nucleotide polymorphisms, tissue markers, and serum markers [14].An HLA-C*06:02 negative genotype was more likely to respond to adalimumab than ustekinumab [15].Conversely, patients achieved a PASI75 outcome with ustekinumab; 92% of HLA-C*06:02 positive patients achieved this compared with 67% of HLA-C*06:02 negative patients [16].Several SNPs were identified as potential biomarkers in response to tumor necrosis factor (TNF)-α inhibitor biologics [17].In recent studies, tissue and serum markers seem to be controversial [18].So far, there is no useful or reliable platform or biomarker that can predict the effectiveness of biologics before starting treatment.
Peripheral blood mononuclear cells (PBMCs), which are composed of lymphocytes, macrophages, dendritic cells, and basophils, are widely used for immune mechanism surveys and drug selection.Each individual's PBMCs present that individual's immune function and characteristics.Therefore, PBMCs are useful in designing an individualized platform for screening the efficacy of biologics before starting treatment.Streptococcus pyogenes (S. pyogenes) can trigger immune responses to activate psoriasis outbreaks [19,20].Furthermore, the innate immune system has been shown to be activated by S. pyogenes in both guttate and chronic plaque psoriasis [21].The specific IgA response against S. pyogenes was correlated with a cutaneous lymphocyte-associated antigen+ T cell-dependent IL-17F response [22].In this study, we use S. pyogenes for PBMC induction to simulate a real clinical psoriasis outbreak and treat it with different biologics.We survey different biomarkers that can represent clinical skin change in terms of the PASI score.

Participants
Our criteria for participating in this study were patients with psoriasis aged from 20 to 65 years old who had experienced at least 2 biological agents.We excluded patients with active infections or potential malignancies.A total of 20 patients with psoriasis with or without psoriatic arthritis were enrolled in this study from 2018 to 2023.All participants were from the clinic of either the Department of Allergy, Immunology, and Rheumatology or the Department of Dermatology at Taichung Veterans General Hospital.All participants provided written informed consent, and the study protocol was approved by the Institutional Review Board of Taichung Veterans General Hospital (TCVGH-CE16265B; TCVGH-CE20043B).The PASI score was accessed before and after the biologic treatments.We recorded the PASI score on day 0 and the PASI score closest to the time of PBMC testing on experienced biologics (at least 180 days) or the PASI score on day 180 for inexperienced biologics or experienced biologics (within 180 days).

Materials
S. pyogenes group A was identified and prepared by the Department of Pathology and Laboratory Medicine of Taichung Veterans General Hospital.S. pyogenes group A was inactivated by heat and then placed on blood agar plates for 1 week.

Cell Culture
To prepare the PBMC culture, 16 mL of blood was obtained from each patient and collected in sodium citrate tubes (Vacutainer CPT, BD Biosciences, Franklin Lakes, NJ, USA).Then, PBMCs were purified using centrifugation at 1800× g for 20 min at room temperature and with the brake off to generate distinct plasma, PBMCs, gel plugs, and red blood cell (RBC) layers.The cells were washed with phosphate-buffered saline and subsequently cultured in RPMI-1640 supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin at 37 • C and 5% CO 2 .

Cell Viability Test
Patient PBMCs were cultured with 5 × 10 6 , 1 × 10 7 , and 2 × 10 7 CFU concentrations of S. pyogenes for 24 h, and then 0.5 mg/mL of 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide was added.After reacting for 1 h, the mixtures were centrifuged, and the supernatants were removed.Subsequently, 200 µL of dimethyl sulfoxide was added to lyse the cells and dissolve purple formazan crystals.We analyzed cell viability with an enzyme-linked immunosorbent assay reader at a wavelength of 570 nm.

Statistical Analysis
All statistical analyses were performed using SPSS version 22 (IBM, Armonk, NY, USA).Cytokine and chemokine levels in psoriasis patients and psoriatic arthritis patients were analyzed using the Mann-Whitney U test.Cytokine expression and PASI were analyzed with Spearman's rho.Data were presented as the mean ± standard deviation.Two-sided p values of 0.05 or less were considered to indicate statistical significance.

Clinical Association between Clinical PASI and Laboratory Profile by Reduction Rate and Difference
Twenty patients were enrolled in our study.The demographic, time of PBMC testing, order of biologic sequences, and clinical characteristics of the psoriasis patients are listed in Table 1.The cytokine analyses of patients' PBMCs and PASI scores are listed in Table 2.In this preliminary pilot study, the PASI reduction rate was strongly correlated with the reduction rate in the IL-13 level (p = 0.001) and the ratios of IFN-γ to IL-13 (p < 0.001), IFNγ to IL-4 (p = 0.019), and IL-17A to IL-13 (p = 0.001).The PASI reduction difference was strongly correlated with the difference in the IFN-γ level (p = 0.002), the difference in the ratios of IFN-γ to IL-4 (p = 0.041), the difference in the ratios of IFN-γ to IL-13 (p = 0.006), the difference in the ratios of IL-17A to IL-4 (p = 0.011), and the difference in the ratios of IL-17A to IL-13 (p = 0.029; Table 3).

Clinical Association between Clinical PASI and Laboratory Profile by Reduction Rate and Difference
Twenty patients were enrolled in our study.The demographic, time of PBMC testing, order of biologic sequences, and clinical characteristics of the psoriasis patients are listed in Table 1.The cytokine analyses of patients' PBMCs and PASI scores are listed in Table 2.In this preliminary pilot study, the PASI reduction rate was strongly correlated with the reduction rate in the IL-13 level (p = 0.001) and the ratios of IFN-γ to IL-13 (p < 0.001), IFN-γ to IL-4 (p = 0.019), and IL-17A to IL-13 (p = 0.001).The PASI reduction difference was strongly correlated with the difference in the IFN-γ level (p = 0.002), the difference in the ratios of IFN-γ to IL-4 (p = 0.041), the difference in the ratios of IFN-γ to IL-13 (p = 0.006), the difference in the ratios of IL-17A to IL-4 (p = 0.011), and the difference in the ratios of IL-17A to IL-13 (p = 0.029; Table 3).The PASI results represent the mean relative PASI.The results of PASI and consumption time were parallel to previous excel columns for different biologics.
In every patient, we showed the absolute PASI score before (day 0) and after biologic treatment.Laboratory profiles showed induction after S. pyogenes and treatment with biologics in different markers.Patient I received secukinumab 150 mg monthly, and patients E and G received secukinumab 300 mg monthly.The patients' PBMCs were treated with secukinumab at 16.7 and 34 µg per milliliter, respectively.

Correlation between Clinical PASI and Biomarkers among Different Biologics
The level of IL-6 was weakly correlated with the PASI score reduction but was not significant.The levels of IL-1β, IL-2, IL-5, IL-7, IL-8, IL-10, IL-12, TNF-α, MCP-1, MIP-1α, MIP-1β, platelet-derived growth factor-BB, and RANTES were not correlated.The relation of biomarkers in different subgroup biologics was that the reduction ratios of IFN-γ to IL-13 were dominant with the PASI reduction difference in ustekinumab (p = 0.047; Figure 2A); the difference in the IL-17A level and the ratio of IL-17A to IL4 were correlated with the PASI difference but were not significant in ustekinumab (p = 0.068, p = 0.052; Figure 2B).The difference in the IL-13 level and the ratio of IFN-γ to IL4 were dominant with the PASI difference in adalimumab (p = 0.01, p = 0.047; Figure 2D).There was a lack of significance for ixekizumab, risankizumab, and guselkumab.
The level of IL-6 was weakly correlated with the PASI score reduction but was significant.The levels of IL-1β, IL-2, IL-5, IL-7, IL-8, IL-10, IL-12, TNF-α, MCP-1, MIP-MIP-1β, platelet-derived growth factor-BB, and RANTES were not correlated.The relat of biomarkers in different subgroup biologics was that the reduction ratios of IFN-γ to 13 were dominant with the PASI reduction difference in ustekinumab (p = 0.047; Fig 2A ); the difference in the IL-17A level and the ratio of IL-17A to IL4 were correlated w the PASI difference but were not significant in ustekinumab (p = 0.068, p = 0.052; Fig 2B).The difference in the IL-13 level and the ratio of IFN-γ to IL4 were dominant with PASI difference in adalimumab (p = 0.01, p = 0.047; Figure 2D).There was a lack of sig icance for ixekizumab, risankizumab, and guselkumab.

Correlation between Clinical PASI and Biomarkers in PsO Only and PsO + PsA Groups
We further investigated biomarkers in the PsO only and PsO + PsA subgroups.S ilar biomarkers, including IFN-r, IL-13, IFN-r/IL-4, IFN-r/IL-13, IL-17A/IL-4, and 17A/IL-13, were significantly associated with clinical PASI, despite fewer samples in e group (Table 4).

Correlation between Clinical PASI and Biomarkers in PsO Only and PsO + PsA Groups
We further investigated biomarkers in the PsO only and PsO + PsA subgroups.Similar biomarkers, including IFN-r, IL-13, IFN-r/IL-4, IFN-r/IL-13, IL-17A/IL-4, and IL-17A/IL-13, were significantly associated with clinical PASI, despite fewer samples in each group (Table 4).

Discussion
Psoriasis is a Th1/Th17-predominant disease.IFN-γ is a crucial cytokine in the pathogenesis of psoriasis.IFN-γ-producing Th1 lymphocytes can cause neutrophil infiltration, vessel proliferation, and keratinocyte hyperproliferation [23].Kryczek et al. found that IFN-γ is a potent promoter of human IL-17+ T cell function, induction, and trafficking [24].IFN-γ synergizing with IL-17 can enhance β-defesin-2 secretion, suggesting that Th1 and Th17 cells can cooperate to contribute to the pathogenesis of psoriasis [25].IFN-γ injections in the unaffected skin of a patient with psoriasis can cause pathogenic T cell infiltration, dendritic cell infiltration, and IL-23 secretion [26].Therefore, IFN-γ is a major cytokine in Th1 and plays a pivotal role in the Th17 pathway.In our pilot research, IFN-γ level was a primary marker reflecting the outcome of biologics before starting treatment.The Th17 pathway is another major effector cytokine in the pathogenesis of psoriatic disease [27].The relationship between the ratio of IL-17A to IL-13 and PASI was also statistically significant.The ratio of IL-17A to IL-13 seems to be another potential marker.Th17/1 cells, which originate from Th17, will be activated in the chronic phase, re-challenged with stress, and then IL-17A and IFN-γ will be secreted to exacerbate clinical disease [28].Taken together, IFN-γ as well as IL-17A are important markers in our research.
Our results suggest that psoriasis is not only associated with Th1 and Th17 but also Th2.An imbalance of Th1 and Th2 tends to exacerbate psoriasis symptoms.In 2003, Ghoreschi et al. developed an innovative therapy where IFN-γ-producing Th1 cells were selectively skewed toward IL-4-producing Th2 cells.Rather than suppressing the immune system, the researchers attempted to achieve immune balance by using IL-4 and found improvements in psoriasis treatment outcomes [29].IL-4 and IL-13 are major cytokines in the Th2 reaction.In the present study, we analyzed the ratios of IFN-γ to IL-4, IFN-γ to IL-13, and IL-17A to IL-13, which were all significantly correlated with clinical PASI scores.By contrast, the levels of IL-8 were not correlated with the PASI score.The relationship between clinical severity and IL-8 concentration has been controversial in some previous studies [30,31].In our study, IL-8 levels were highly sensitive in S. pyogenes induction, and some data appeared out of range in multiplex testing.Beyond that, our data showed no correlation with clinical PASI scores.Some biomarkers similar to IL-8, such as MCP-1, MIP-1α, and MIP-1β, were very sensitive to induction by S. pyogenes, independent of PASI results.IL-8, MCP-1, MIP-1α, and MIP-1β are essential for chemotaxis.In our opinion, variation in Th1 or Th17 upstream responses appears to correlate more with clinical PASI scores than cell chemotaxis.
In our subgrouping data, different markers appear to be present in different biologics.IFN-γ/IL-4 and IL-13 are important biomarkers in adalimumab.IFN-γ/IL-13 is an important one in ustekinumab.In different biologic mechanisms, they share the same biomarkers on IFN-γ and IL-13.Additionally, IL-17A seemed to be another marker for ustekinumab from our subgroup data.IFN-γ plays a pivotal role in the Th1 pathway.Adalimumab is a TNF-α inhibitor, a major cytokine in the Th1 pathway.It is reasonable that IFN-γ is an important marker of adalimumab.Ustekinumab blocks IL-12 and IL-23, which are upstream of Th17.It is logical that IL-17A is a better marker for ustekinumab than adalimumab.IFN-γ levels have a role in determining disease severity, as shown in a previous study [32].The IL-17 levels of psoriasis patients were significantly higher than those of the controls and correlated to the severity of the disease [33].IL-13 is an important cytokine of Th2, which plays important parts in both ustekinumab and adalimumab in our study.All of them are potentially important predictive biomarkers for guiding treatment selection.The lack of significance for ixekizumab, risankizumab, and guselkumab, respectively, may be due to the small sample size.
Appropriate biological therapy for individual patients with psoriasis can interrupt multiple inflammatory loops.TNF-α inhibitors could block dendritic cell activation, thereby inhibiting Th1-induced IFN-γ secretion and Th17-induced IL-17 secretion [34].The exhausted phenotype of regulatory T cell dysfunction comprised reduced TGF-β release and increased IFN-γ production.Blocking IL-17 will reverse the exhausted phenotype of regulatory T cells to reverse the increased IFN-γ levels [35][36][37][38].Furthermore, the induction of IL12p70 production on macrophages by IL-17 potentiates IFN-γ production by CD4+ cells.IL-17 inhibitor biologics could reduce IFN-γ production from macrophages and CD4+ cells [39].IL-23, or IL-12/23, is the upstream cytokine of IL-17; the inhibitors of these share the same mechanism for blocking IFN-γ.In clinical data, we also observed suppression of serum IFN-γ levels with ustekinumab treatment in systemic lupus erythematosus patients [40].The mechanisms of different biologics on IL-4 and IL-13 levels in individual patients with psoriasis remain unclear and need to be elucidated in the future.The role of immunity in Th1, Th17, and Th2 is frequently seen as a Yin and Yang [41].Selecting a biologic that downregulates IFN-γ and IL-17A and upregulates IL-13 and IL-4 leads to better outcomes in individually tailored treatments.By prescreening patients, we could select a suitable biologic for an individual patient by following the flow chart in Figure 3.This study has some limitations.First, the results of the study must be validated in a larger group to assess the variability and validity of our findings.Second, some measurement errors can occur when the concentrations of IL-13 or IL-4 are extremely low, which may have caused the extremely large ratios of IL-17A to IL-13 and IFN-γ to IL-13 or IL-4 in some of our observations.More clinical research is necessary to address these limitations.
Appropriate biological therapy for individual patients with psoriasis can interrupt multiple inflammatory loops.TNF-α inhibitors could block dendritic cell activation, thereby inhibiting Th1-induced IFN-γ secretion and Th17-induced IL-17 secretion [34].The exhausted phenotype of regulatory T cell dysfunction comprised reduced TGF-β release and increased IFN-γ production.Blocking IL-17 will reverse the exhausted phenotype of regulatory T cells to reverse the increased IFN-γ levels [35][36][37][38].Furthermore, the induction of IL12p70 production on macrophages by IL-17 potentiates IFN-γ production by CD4+ cells.IL-17 inhibitor biologics could reduce IFN-γ production from macrophages and CD4+ cells [39].IL-23, or IL-12/23, is the upstream cytokine of IL-17; the inhibitors of these share the same mechanism for blocking IFN-γ.In clinical data, we also observed suppression of serum IFN-γ levels with ustekinumab treatment in systemic lupus erythematosus patients [40].The mechanisms of different biologics on IL-4 and IL-13 levels in individual patients with psoriasis remain unclear and need to be elucidated in the future.The role of immunity in Th1, Th17, and Th2 is frequently seen as a Yin and Yang [41].Selecting a biologic that downregulates IFN-γ and IL-17A and upregulates IL-13 and IL-4 leads to better outcomes in individually tailored treatments.By prescreening patients, we could select a suitable biologic for an individual patient by following the flow chart in Figure 3.This study has some limitations.First, the results of the study must be validated in a larger group to assess the variability and validity of our findings.Second, some measurement errors can occur when the concentrations of IL-13 or IL-4 are extremely low, which may have caused the extremely large ratios of IL-17A to IL-13 and IFN-γ to IL-13 or IL-4 in some of our observations.More clinical research is necessary to address these limitations.

Conclusions
Psoriasis is a complex disease associated with genetics, environmental factors, and immune reactions, influencing diverse clinical characteristics and responses to treatment.We created an innovative, economic, and convenient platform to prescreen the efficacy of biological treatments for individual patients.The markers IFN-γ, IL-13, IFN-γ/IL13, IL-17A/IL-13, IFN-γ/IL4, and IL-17A/IL-4 are representative of predicting the efficacy of psoriasis treatment.These markers imply the importance of not only the Th1/Th17 pathway but also the Th2 pathway.Although the cohort analyzed was small, this study may be a valuable step toward predicting the responses of patients with psoriasis to a given therapy and, thus, toward individually tailored treatments.

Figure 2 .
Figure 2. Correlation between clinical PASI and biomarkers among different biologics.Heatm showing correlation coefficients with different colors and p values with stars.(A) Reduction ra of PASI and biomarkers for ustekinumab.(B) Reduction difference of PASI and biomarkers ustekinumab.(C) Reduction ratios of PASI and biomarkers for adalimumab.(D) Reduction diff ence of PASI and biomarkers for adalimumab.* p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 2 .
Figure 2. Correlation between clinical PASI and biomarkers among different biologics.Heatmaps showing correlation coefficients with different colors and p values with stars.(A) Reduction ratios of PASI and biomarkers for ustekinumab.(B) Reduction difference of PASI and biomarkers for ustekinumab.(C) Reduction ratios of PASI and biomarkers for adalimumab.(D) Reduction difference of PASI and biomarkers for adalimumab.* p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 3 .
Figure 3. Suggested protocol for biologic selection before psoriasis treatment.

Figure 3 .
Figure 3. Suggested protocol for biologic selection before psoriasis treatment.

Table 1 .
Demographic and clinical characteristics of psoriasis patients (n = 20).

Table 1 .
Demographic and clinical characteristics of psoriasis patients (n = 20).

Table 3 .
Clinical association between clinical PASI and laboratory profile by reduction rate and difference.

Table 4 .
Correlation between clinical PASI and biomarkers in PsO only and PsO + PsA groups.